DETECTING COGNITIVE IMPAIRMENT FROM DIGITAL VOICE RECORDINGS OF A REPETITION TASK

Abstract Early detection of Alzheimer’s disease is needed but cognitive screeners, used widely in research and clinical settings, suffer from poor sensitivity and specificity. Acoustic features of speech are altered in Alzheimer’s disease and may aid detection of cognitive impairment. We sought to test whether acoustic metrics from a cognitive screener differentiate individuals with cognitive impairment from those who are cognitively normal. We extracted 38 acoustic features from digital voice recordings of the Mini-Mental Status Examination repetition task from Long Life Family Study participants using the openSMILE toolkit. Highly correlated features (r>0.9) were removed. Cognitive status (impaired versus cognitively normal) was determined through consensus review of neuropsychological testing and informant interviews. Bayesian Information Criterion-based stepwise regression including 24 acoustic variables and retaining age, sex, education, and MMSE score in all models was used to identify predictors of cognitive status. Acoustic features from preliminary data included 277 participants (mean age 74.8±12.3 years old, 57% females, 34% cognitively impaired due to oversampling). Stepwise selection identified greater loudness (i.e., signal intensity; p<0.001), smaller pitch changes (p=0.007), and lower Harmonics-to-Noise Ratio (i.e., a metric of voice quality; p=0.017) as significantly associated with cognitive impairment. A model with MMSE, demographics, and acoustics had a higher discriminative value for cognitive status (AUC 0.95, sensitivity=0.97, specificity=0.73) than the model without acoustics (AUC = 0.94, sensitivity=0.97, specificity=0.63). Overall, acoustic features from a brief (i.e., 3 second) repetition task were associated with cognitive impairment and added information beyond traditional scoring that increased the specificity of a cognitive screener.

Nursing, Baltimore,Maryland,United States,2. Johns Hopkins School of Medicine,Baltimore,Maryland,United States,3. Johns Hopkins School of Public Health,Baltimore,Maryland,United States While clinicians understand the importance of ACP, it is difficult to overcome logistical and ethical challenges in the limited time providers have in primary clinic appointments with older adults with ADRD and other complex co-morbidities.Ethical/moral dilemmas faced by patients, families, and clinicians are reflected in the four principles of biomedical ethics including autonomy, beneficence, nonmaleficence, and justice.While ethical challenges are recognized there is a lack of real-world evidence to guide clinicians or family caregivers in ACP for older adults with ADRD based on the reality of complex patient and family dynamics.Throughout the SHARE trial, facilitated ACP conversations with trained ACP facilitators, older adult patients with cognitive impairment, and their family caregiver were audio recorded and transcribed.The purpose of this presentation is to expand our understanding of best practices for facilitating ACP conversations among persons with ADRD and their family caregivers, guided by medical ethics and the Learning Health Care System Ethics Framework.We draw on the unique dataset of recorded ACP conversations involving older adults with severe cognitive impairment (N=92, average age=88 years), family caregivers, and trained ACP facilitators.We qualitatively coded transcripts for ethical considerations of ACP conversations and conducted the thematic analysis.We will present major themes related to ethical considerations in facilitating ACP conversations among persons with moderate to severe ADRD and their family caregivers.We will provide a discussion framed in balancing patient, family, and provider needs within a Learning Health Care System.

CATALYZING NOVEL METHODS OF MEASURING COGNITION BY HARNESSING TECHNOLOGY
Chair: Stacy Andersen Co-Chair: Walter Boot Early detection of cognitive impairment is essential for enabling access to clinical assessments and treatments and facilitating participation in clinical trials for Alzheimer's disease.By employing smartphones and digital technologies to capture cognitive behaviors with high precision we may be able to detect novel, subtle biomarkers of cognitive impairment during clinic-based assessments, in-home selfadministered tasks, and daily activities.In this Collaborative Symposium from the Alzheimer's Disease and Related Dementias and Technology and Aging Interest Groups, we will explore the development of technologies to detect cognitive impairment and discuss the feasibility of obtaining these data in non-traditional settings.The first presentation reports on the use of eye-tracking technology to detect cognitive impairment with high accuracy.The second presentation discusses acoustic features from digital voice recordings of a cognitive screener that are associated with cognitive impairment and increase the screener's specificity.The next presentation describes linguistic features from voice recordings of a memory test that are associated with depression and distinct from those associated with cognitive impairment.The fourth presentation introduces a smartphone application that measures hand grip strength, a known correlate of dementia risk.The symposium concludes with a summary of older adults' attitudes toward wearable technologies to detect cognitive decline and the development of a smart reminder system to promote adherence to home-based assessment.Overall, these presentations highlight ways we can harness technology to capture unique features of cognitive function that increase the accuracy of traditional testing or detect cognitive impairment in novel settings.This is a collaborative symposium between the Alzheimer's Disease and Related Dementias and Technology and Aging Interest Groups.

USE OF EYE TRACKING TECHNOLOGY TO DETECT MILD COGNITIVE IMPAIRMENT
Quinn Kennedy 1 , Linda Chao 2 , and Dorion Liston 3 , 1. neuroFit, Carmel, California, United States, 2. University of California,San Francisco,San Francisco,California,United States,3. neuroFit,Mountain View,California,United States Early detection of dementia is key to helping individuals and their families cope.Use of eye tracking technology to measure eye movements could provide an objective and sensitive measure of cognitive impairment (Hodgson et al, 2019;Wang et al, 2020).In this pilot study, we predicted that eye tracking metrics differ between people with diagnosed MCI and those of healthy controls.Eleven veterans (≥ 55 years) being seen at the San Francisco VA Health Care System who either had a confirmed diagnosis of MCI or had subjective memory complaints and scored lower than a 26 on the MoCA participated.Their results were compared to that of a previously collected control sample (n = 41) (Liston et al, 2017).Participants completed a five-minute visual tracking test with 48 trials based on a classic stepramp visual tracking paradigm that was administered on a PC computer with a camera and eye tracking capability.The visual tracking test yields 10 z-scored eye tracking metrics that are summarized in a single scalar summary score.Receiver operating characteristics (ROC) were computed and compared to the control sample.The summary score of the MCI group (median: -2.2) differed significantly from the healthy control sample (median: 0.0), which yielded a significant sensitivity of the test to presence or absence of MCI (ROC area = 0.94, p < .001).Although we view the results as preliminary due to the small sample size, results suggest that use of eye tracking technology may be a viable option for MCI detection.
United States,3. Boston University,Boston,Massachusetts,United States,4. Columbia University Medical Center,New York City,New York,United States,5. Boston University Chobian & Avedisian School of Medicine,Boston,Massachusetts,United States,6. Tufts Medical Center,Boston,Massachusetts,United States Early detection of Alzheimer's disease is needed but cognitive screeners, used widely in research and clinical settings, suffer from poor sensitivity and specificity.Acoustic features of speech are altered in Alzheimer's disease and may aid detection of cognitive impairment.We sought to test whether acoustic metrics from a cognitive screener differentiate individuals with cognitive impairment from those who are cognitively normal.We extracted 38 acoustic features from digital voice recordings of the Mini-Mental Status Examination repetition task from Long Life Family Study participants using the openSMILE toolkit.Highly correlated features (r>0.9) were removed.Cognitive status (impaired versus cognitively normal) was determined through consensus review of neuropsychological testing and informant interviews.Bayesian Information Criterion-based stepwise regression including 24 acoustic variables and retaining age, sex, education, and MMSE score in all models was used to identify predictors of cognitive status.Acoustic features from preliminary data included 277 participants (mean age 74.8±12.3 years old, 57% females, 34% cognitively impaired due to oversampling).Stepwise selection identified greater loudness (i.e., signal intensity; p<0.001), smaller pitch changes (p=0.007), and lower Harmonics-to-Noise Ratio (i.e., a metric of voice quality; p=0.017) as significantly associated with cognitive impairment.A model with MMSE, demographics, and acoustics had a higher discriminative value for cognitive status (AUC 0.95, sensitivity=0.97,speci-ficity=0.73)than the model without acoustics (AUC = 0.94, sensitivity=0.97,specificity=0.63).Overall, acoustic features from a brief (i.e., 3 second) repetition task were associated with cognitive impairment and added information beyond traditional scoring that increased the specificity of a cognitive screener.

LINGUISTIC PROFILES OF PARAGRAPH RECALL PREDICT DEPRESSION IN OLDER ADULTS
Seho Park 1 , Nicole Roth 2 , Paola Sebastiani 3 , Stephanie Cosentino 4 , and Stacy Andersen 1 , 1. Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, United States, 2. Boston University School of Medicine,Boston,Massachusetts,United States,3. Tufts Medical Center,Boston,Massachusetts,United States,4. Columbia University Medical Center,New York City,New York,United States Our previous work identified linguistic markers from paragraph recall that predicted cognitive impairment.However, many features were related to psychological constructs, and mood disorders, such as depression, are also associated with cognitive impairment.Therefore, we aimed to identify linguistic markers that are associated with depression and examine their overlap with those for cognitive impairment.We used Linguistic Inquiry Word Count, a natural language processing tool, on Logical Memory transcriptions from the Long Life Family Study.Depression status was positive for participants with a self-reported depression diagnosis or high depressive symptoms (i.e., a CESD-10 score≥10) at testing.We used logistic regression models with Generalized Estimating Equations adjusted by age, sex, education, and within-family relatedness to identify linguistic features associated with depression status (Bonferronicorrected threshold of p<.001).The sample included 61 participants with evidence of depression and 539 participants without (mean age=72.1±10.9years, female=63%).A lower percentage of verb use in delayed recall was significantly associated with depression (Odds Ratio [OR]=0.71,CI 0.55-0.93,p=.0001).Additionally, 3 immediate recall features (verbs, OR=0.71; prepositions, OR=1.30; differentiation words, OR=1.30) and 5 delayed recall features (function words, OR=0.68; prepositions, OR=1.50; future tense, OR=0.48; cognitive process words, OR=0.78; past tense, OR=0.74) were significant at a nominal level (p=.009 to p=.035).We identified linguistic features from paragraph recall responses that predict depression and were largely distinct from those associated with cognitive impairment as only two features overlapped.Linguistic analyses of spoken responses may help us understand contributors to test performance and distinguish between cognitive impairment and depression.

CHALLENGES AND NOVEL SOLUTIONS TO PROMOTING ADHERENCE TO HOME-BASED COGNITIVE ASSESSMENT
Walter Boot, Ibukun Fowe, Shenghao Zhang, Michael Dieciuc, and Andrew Dilanchian, Florida State University, Tallahassee, Florida, United States New technologies now allow for home-based self-assessment of cognition to promote the early detection and treatment of cognitive decline.Early detection has a multitude of benefits for the individual and for our scientific understanding of cognitive impairments and possible treatments.However, for these solutions to be effective, they will likely require long-term adherence to assessment protocols to detect longitudinal change, and like many health behaviors, adherence can be a major challenge.This presentation will provide an overview of the Adherence Promotion with Person-centered Technology (APPT) project, which aims to develop a tailored, smart reminder system that infers participants' contexts and motivations to provide just-in-time support to facilitate cognitive assessment adherence.Among other studies, this overview will include discussion of a recently completed focus group study (N = 42) that was used to understand motivations and potential barriers to homebased assessment and preferences for adherence support, a recently completed pilot study (N = 44) in which the adherence support system was initially tested and refined, and a series of survey studies (total N > 200) that examined older adults' attitudes toward wearable technologies to detect cognitive decline.The talk will conclude with discussion of an upcoming randomized controlled trial to test the efficacy of the newly developed system with respect to promoting long-term adherence to cognitive assessment via smartphone technologies.